Vehicle data collection and verification

ABSTRACT

Disclosed are various embodiments for a data aggregation application. Operational data and image data may be captured from a client device. Odometer readings can be extracted from the image data. The operational data and image data can be verified by comparing an instrument panel depicted in the image data to a known instrument panel depiction.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims the benefit of andpriority to U.S. patent application Ser. No. 14/933,260 entitled“VEHICLE DATA COLLECTION AND VERIFICATION”, filed on Nov. 5, 2015, U.S.patent application Ser. No. 13/829,140 entitled “VEHICLE DATA COLLECTIONAND VERIFICATION”, filed on Mar. 14, 2013 now U.S. Pat. No. 9,183,441issued on Nov. 10, 2015, U.S. Provisional Applications 61/696,116,“AUTHENTICATION OF IMAGE-DERIVED VEHICLE DATA”, filed on Aug. 31, 2012and 61/663,756, “IMAGE-BASED VEHICLE ODOMETER REPORTING AND ASSOCIATEDDATA COLLECTION”, filed on Jun. 25, 2012, which are hereby incorporatedby reference in their entirety, as if fully set forth herein.

BACKGROUND

Obtaining data relating to vehicles such as odometer readings andoperator behavioral data can be beneficial to issuers of insurancepolicies and other service providers. Using specialized sensors ordevices to perform the verification adds additional costs andcomplications for a user.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the present disclosure can be better understood withreference to the following drawings. The components in the drawings arenot necessarily to scale, with emphasis instead being placed uponclearly illustrating the principles of the disclosure. Moreover, in thedrawings, like reference numerals designate corresponding partsthroughout the several views.

FIG. 1 is a drawing of a networked environment according to variousembodiments of the present disclosure.

FIG. 2 is a flowchart illustrating one example of functionalityimplemented as portions of a data aggregation application executed in acomputing environment in the networked environment of FIG. 1 accordingto various embodiments of the present disclosure.

FIG. 3 is a flowchart illustrating one example of functionalityimplemented as portions of a data aggregation application executed in acomputing environment in the networked environment of FIG. 1 accordingto various embodiments of the present disclosure.

FIG. 4 is a schematic block diagram that provides one exampleillustration of a computing environment employed in the networkedenvironment of FIG. 1 according to various embodiments of the presentdisclosure.

DETAILED DESCRIPTION

Insurance policies for automobiles are often issued for a particularautomobile owned by a customer. The terms and conditions of an insurancepolicy may be dependent on the type of automobile covered by the policy.For example, an insurance policy for an automobile known to have greatersafety risks may require a higher premium when compared to a policy fora lower risk automobile. The terms and conditions of the insurancepolicy may also be dependent on driving habits of a user. For example,the policy issuer may offer incentives for customers who drive a limiteddistance, limit their driving speed to a predefined limit, or maintaintheir automobiles in proper working condition. Additionally, incentivesor discounts may be offered to vehicle operators who refrain fromcertain behaviors, such as operating a mobile phone during operation ofthe vehicle.

Monitoring the type, operation, and mileage of an automobile may beperformed by implanting proprietary sensors into the automobile. Thiscomes at a financial cost to either the insurance company or thecustomer to cover the cost of the sensors and their installation.Additionally, a customer may be discouraged from installing the sensorsfor fear of impacting the automobile's performance, or reducing theresale value of the automobile.

By using sensors commonly available in mobile devices such as mobilephones, a data aggregation application can obtain images of an operatedvehicle and other data from a client device. For example, a client cansubmit a picture of an instrument panel such as a dashboard from whichan odometer reading is extracted. Data embodying driving habits can begenerated from global positioning system data, accelerometer data,cellular or other wireless connections, and potentially other data.Behavior patterns such as talking, texting, or otherwise operating themobile device can also be detected.

A service provider wishing to aggregate this data may also wish toverify the integrity of the data. To this end, the data aggregationapplication may verify that the automobile from which the data iscaptured corresponds to the automobile or type or class of automobilecovered by the service. This may be performed by detecting a uniqueidentifier captured by image data, such as a vehicle identificationnumber. This may also be performed by comparing an image of a capturedinstrument panel to a knowledge base of identified instrument panels.The application may also detect whether an image has been altered ormodified after being captured. Other approaches may also be used toverify the integrity of the data.

In the following discussion, a general description of the system and itscomponents is provided, followed by a discussion of the operation of thesame.

With reference to FIG. 1, shown is a networked environment 100 accordingto various embodiments. The networked environment 100 includes acomputing environment 101, and a client 104, which are in datacommunication with each other via a network 107. The network 107includes, for example, the Internet, intranets, extranets, wide areanetworks (WANs), local area networks (LANs), wired networks, wirelessnetworks, or other suitable networks, etc., or any combination of two ormore such networks.

The computing environment 101 may comprise, for example, a servercomputer or any other system or device providing computing capability.Alternatively, the computing environment 101 may employ a plurality ofcomputing devices that may be employed that are arranged, for example,in one or more server banks or computer banks or other arrangements.Such computing devices may be located in a single installation or may bedistributed among many different geographical locations. For example,the computing environment 101 may include a plurality of computingdevices that together may comprise a cloud computing resource, a gridcomputing resource, and/or any other distributed computing arrangement.In some cases, the computing environment 101 may correspond to anelastic computing resource where the allotted capacity of processing,network, storage, or other computing-related resources may vary overtime.

Various applications and/or other functionality may be executed in thecomputing environment 101 according to various embodiments. Also,various data is stored in a data store 111 that is accessible to thecomputing environment 101. The data store 111 may be representative of aplurality of data stores 111 as can be appreciated. The data store canbe anywhere on any one or more storage devices in the environment 100and accessible to or by the computing environment 101. The data storedin the data store 111, for example, is associated with the operation ofthe various applications and/or functional entities described below.

The components executed on the computing environment 101, for example,can include a data aggregation application 114 having a communicationsmodule 117, a data extraction module 121, and a verification module 124,and other applications, services, processes, systems, engines, orfunctionality not discussed in detail herein. The data aggregationapplication 114 is executed to obtain monitoring data 127 from a client104 to be associated with a user account 128.

For example, the monitoring data 127 may comprise image data 131capturing an image of at least one component of a vehicle. For example,the image data 131 may depict an instrument panel such as a dashboard ora meter such as an odometer. The image data 131 may also capture someidentifier affixed to a vehicle, such as a vehicle identification number(VIN) or other identifier. The identifier may be represented as analphanumeric value, barcode, Quick Response (QR code), or anotherrepresentation.

The image data 131 may be encoded as a single frame image, or as amultiple frame movie file. Additionally, the image data 131 may beencoded with or otherwise associated with metadata 134. Metadata 134associated with the image data 131 may comprise, without limitation, adate or time at which the image data 131 was generated or dataindicative of a location at which the image data 131 was generated.Metadata 134 associated with the image data 131 may also comprise anidentifier of a client 104 or other device which generated the imagedata 131, a camera resolution or shutter speed, or other characteristicsof the image data 131 or a device for generating the image data 131.

The image data 131 may be captured by a component of a client 104 suchas a built-in or peripheral camera. The image data 131 may also begenerated by another device and then communicated to the dataaggregation application 114. For example, the image data 131 may begenerated by a dedicated digital camera, and then transferred to orotherwise made accessible to a client 104 for communication to the dataaggregation application 114.

The monitoring data 127 may also comprise operational data 137indicative of vehicle operations, date, time and duration of vehicleoperation, or operator behavior patterns with respect to a vehicle. Forexample, operational data 137 may comprise data generated by anaccelerometer, clock, or global positioning system (GPS) radio sensoraccessible to the client 104. The accelerometer, clock, GPS radio orother sensor used to generate the operational data 137 may be acomponent of the client 104, or a sensor distinct from but otherwiseaccessible to the client 104. For example, the client 104 may be incommunication with a GPS radio built in to the vehicle for obtaining GPSdata for inclusion in the operational data 137.

The operational data 137 may also comprise usage data relating to anoperation of the client 104. For example, in embodiments in which theclient 104 comprises a mobile phone, the operational data 137 mayinclude data representing talking, texting, internet accessing, or otheractions taken by the client 104. A variety of sensors may thus be usedto generate operational data 137, for example a magnetometer, agyroscope, a global positioning device or other sensor. For example,location operational data 137 may be determined by use of cell towerdata or tower triangulation, involving a data source and/or a backendsystem apart from a location sensor.

The monitoring data 127 may also comprise additional metadata 134related to the monitoring data 127, the client 104, or the associatedvehicle. For example, the metadata 134 may comprise hardware componentdata, operating system or software version data, wireless carrier ornetwork information, or other data associated with the client 104. Themetadata 134 may also comprise account, session, or login informationassociated with a user of the client 104. The metadata 134 may furthercomprise data embodying cellular towers, wireless access points, orother networking components to which the client 104 has connected. Themetadata 134 may also comprise other data as well.

The monitoring data 127 may then be stored with respect to an account128. Storing the monitoring data 127 may be performed responsive to asuccessful verification by the verification module 124 or responsive tosome other criteria.

A smart mobile device may be used for sensing or obtaining themonitoring data 127. Examples of a suitable smart mobile device includesmart mobile phones, tablets, personal digital assistants (PDA's), orother portable devices with electronic processing capability. Suchdevices may include any one or more sensors for sensing a characteristicof the vehicle to be monitored such as an automobile. For example, suchdevices may include one or more sensors such as an audio, motion,vibration, orientation and location sensors.

The data extraction module 121 generates additional data from themonitoring data 127 obtained by the communications module 117. Forexample, the data extraction module 121 may apply a text or opticalrecognition function to an image data 131 depicting an instrument panelor odometer to generate odometer data 141 indicative of an odometerreading at the time at which the image data 131 was generated. The dataextraction module 121 may also apply a text or optical recognitionalgorithm, QR code recognition algorithm, barcode recognition algorithm,or other image analysis algorithm to extract a vehicle identifier 144from the image data 131. The vehicle identifier 144 comprises anencoding of a unique identifier associated with a vehicle, such as aVIN, as well as potentially data embodying a year, make, model and colorof a vehicle, or other data.

The data extraction module 121 may also infer or detect unsafeoperational behavior patterns from the operational data 131. Forexample, operational data 131 comprising GPS or cell tower dataindicating a moving vehicle at a time during which further operationaldata 131 indicates texting or talking via a client 104 may indicate thata vehicle operator was texting during operation. As another example, GPSoperational data 137 indicating a particular movement distance may becorrelated with odometer data 141 to detect deviations between reportedand detected mileage. Other data may also be generated by the dataextraction module 121.

Data extracted by the data extraction module 121 such as odometer data141 or a vehicle identifier 144 may then be stored with respect to anaccount 128. Storing this data may be performed responsive to asuccessful verification by the verification module 124 or responsive tosome other criteria.

The verification module 124 verifies the integrity of the monitoringdata 127 to confirm that the monitoring data 127 was obtained from orembodies a vehicle associated with an account 128. For example, theverification module 124 may compare a vehicle identifier 144 generatedby the data extraction module 121 and compare that value to a vehicleidentifier 144 stored with respect to an account 128.

In another embodiment, the verification module 124 may determine if animage data 131 embodying an instrument panel or dashboard corresponds toa vehicle identifier 144 stored with respect to an account 128. This maycomprise, for example, comparing the image data 131 to an imageknowledge base 147 storing image data 131 of instrument panels ordashboards corresponding to the vehicle identifier 144. Entries in theimage knowledge base 147 may depict the instrument panel of the vehicledefined by the vehicle identifier 144. For example, upon account 128creation, one or more instances of image data 131 may be captured forthe instrument panel of the vehicle for later comparison. In someembodiments, entries in the image knowledge base 147 may compriseinstrument panels or dashboards of vehicles sharing a like make, model,or year with respect to the vehicle defined by the vehicle identifier144. Comparisons of image data 131 to an image knowledge base 147 may beperformed by a machine learning algorithm, such as but not limited to asupport vector machine (SVM). Comparisons of image data 131 to an imageknowledge base 147 may also be performed by an image comparison ormatching algorithm or other algorithm as can be appreciated.

Image knowledge base 147 entries may be specifically developed for useby the data aggregation application 114 or may be captured from publiclyavailable sources such as the Internet. The image knowledge base 147 maybe stored locally within the environment of the present system orremotely (for example in a cloud-based system) or simply obtained bysearching or crawling publicly available data bases.

The verification module 124 may also perform login or other identityverifications associated with a submission of monitoring data 127. Thismay comprise performing password verifications, security questionverification, facial recognition or other identity verifications. Thismay also comprise comparing metadata 134 of the monitoring data 127 toknown attributes of the account 128. For example, an account 128 may beassociated with a defined client 104 identified by some uniqueidentifier or hardware specification embodied in metadata 134. Theverification module 124 may then compare the metadata 134 of themonitoring data 127 to the known client 104 parameters associated withthe account 128.

The verification module 124 may also check the integrity of image data131 to determine if the image data 131 was altered or otherwise modifiedprior to submission to the data aggregation application 114. This maycomprise, for example, performing edge detection, artifact detection,pixel pattern detection, or other method to detect image modificationsas can be appreciated.

The verification module 124 may also verify the integrity of a generatedodometer data 141 as being greater than or equal to a previouslygenerated odometer data 141 or a known odometer data 141 associated witha temporally earlier time with respect to the generated odometer data141.

The data stored in the data store 111 includes, for example, accounts128 having one or more of an odometer data 141, operational data 137,metadata 134, or a vehicle identifier 144. The accounts 128 may bestored in a relational database or in another data structure as can beappreciated. An image knowledge base 147 may also be stored in the datastore 111.

The client 104 is representative of a plurality of client devices thatmay be coupled to the network 107. The client 104 may comprise, forexample, a processor-based system such as a computer system. Such acomputer system may be embodied in the form of, a laptop computer,personal digital assistants, cellular telephones, smartphones, personalnavigation devices, music players, web pads, tablet computer systems,electronic book readers, or other mobile devices with electronicprocessing capability. Although the client 104 comprises a smartphone ina preferred embodiment, it is understood that the client 104 maycomprise any device with like capability.

The client 104 may be configured to execute various applications such asa client application 151 and/or other applications. The clientapplication 151 is executed to generate monitoring data 127 associatedwith a vehicle. This may comprise, for example, generating image data131 from a built-in or peripheral camera or obtained from anotherdevice. The client application 151 may also generate operational data131 by accessing sensors including a GPS sensor, cellular network radio,other network radio, accelerometer, or other sensors. Such sensors maybe components of the client 104 or remotely accessible to the client104.

The client application 151 also facilitates the communication ofmonitoring data 127 to the data aggregation application 114. Themonitoring data 127 may be communicated responsive to user input,responsive to an electronic message received from an external system orcomputing environment including without limitation computing environment101, at a predefined interval, or at a predefined time. The clientapplication 151 may also restrict generation or communication ofmonitoring data 127 to predefined conditions, such as the client 104being connected to a power supply, or other conditions. The conditionsmay be predefined by the client application 151 or defined as a userpreference.

Referring next to FIG. 2, shown is a flowchart that provides one exampleof the operation of a portion of the data aggregation application 114(FIG. 1) according to various embodiments. It is understood that theflowchart of FIG. 2 provides merely an example of the many differenttypes of functional arrangements that may be employed to implement theoperation of the portion of the data aggregation application 114 asdescribed herein. As an alternative, the flowchart of FIG. 2 may beviewed as depicting an example of steps of a method implemented in thecomputing environment 101 (FIG. 1) according to one or more embodiments.

Beginning with box 201, the data aggregation application 114 obtainsmonitoring data 127 (FIG. 1) via a network 107 (FIG. 1) from a client104 (FIG. 1) executing a client application 157 (FIG. 1). In thisexample, the monitoring data 127 comprises image data 131 (FIG. 1)depicting an odometer of a vehicle, and image data 131 depicting avehicle identification number (VIN) of a vehicle. Next, in box 204, thedata extraction module 121 (FIG. 1) generates odometer data 141 (FIG. 1)from the image data 131. In some embodiments, this comprises applying animage recognition algorithm to the image data 131 to determine anumerical odometer reading embodied in the image data. Generating theodometer data 141 may also be performed by another approach.

The data extraction module 121 then generates a vehicle identifier 144(FIG. 1) from the monitoring data 127 in box 207. In some embodiments,this comprises applying an image recognition algorithm to image data 131to extract an alphanumeric vehicle identifier 144. In other embodiments,this comprises applying an algorithm to convert a visual encoding of avehicle identifier 144 such as a QR code or barcode. The vehicleidentifier 144 may also be generated from image data 131 by anotherapproach.

In box 211, the verification module 124 (FIG. 1) verifies theauthenticity of the monitoring data 127 by comparing the vehicleidentifier 144 generated by the data extraction module to a vehicleidentifier 144 defined in a user account 128 (FIG. 1). After themonitoring data 127 has been verified, the monitoring data 127 and theextracted odometer data 141 is stored with respect to an account 128.This may comprise creating or updating an entry in a database associatedwith the account 128, or taking some other action with respect to theaccount 128.

Referring next to FIG. 3, shown is a flowchart that provides one exampleof the operation of a portion of the data aggregation application 114(FIG. 1) according to various embodiments. It is understood that theflowchart of FIG. 3 provides merely an example of the many differenttypes of functional arrangements that may be employed to implement theoperation of the portion of the data aggregation application 114 asdescribed herein. As an alternative, the flowchart of FIG. 3 may beviewed as depicting an example of steps of a method implemented in thecomputing environment 101 (FIG. 1) according to one or more embodiments.

Beginning with box 301, the data aggregation application 114 obtainsmonitoring data 127 (FIG. 1) via a network 107 (FIG. 1) from a client104 (FIG. 1) executing a client application 151 (FIG. 1). The monitoringdata 127 is associated with an account 128 (FIG. 1) indicated by theclient 104. Furthermore, in this example, the monitoring data 127comprises image data 131 capturing a dashboard of a vehicle andpotentially other data. Next, in box 304, the verification module 124(FIG. 1) queries an image knowledge base 147 (FIG. 1) with the imagedata 131 of the dashboard to identify a vehicle embodied in the imagedata 131 of the dashboard. In some embodiments, this comprises applyingan image matching or machine learning algorithm to the image knowledgebase 147. The image knowledge base 147 may be stored in a data store 111(FIG. 1) accessible to the verification module 124, aggregated byexecuting a web crawler or other functionality, or accessible by someother approach. Querying the image knowledge base 147 may return avehicle identifier 144 (FIG. 1), a make, model, or year of a vehicle, orpotentially other data corresponding to a matching vehicle.

In box 307, the monitoring data 127 is authenticated with respect to theidentified account 128 as a function of the image knowledge base 147identification and metadata 134 (FIG. 1) encoded in the monitoring data127. This may comprise, for example, determining if a vehicle embodiedin the image knowledge base query 147 matches a vehicle defined in avehicle identifier 144 (FIG. 1) of the account 128. This may furthercomprise comparing metadata 134 embodying client 104 characteristics toknown client 104 attributes associated with the account 128. Forexample, this may comprise determining if the monitoring data 127 wasgenerated from a client 104 connected to a predefined wireless carrieror matching a known hardware profile. Other approaches may also be usedto authenticate the monitoring data 127.

In box 310, the verification module 124 verifies the integrity of theimage data 131 by determining if the image data 131 was altered orotherwise modified prior to submission to the data aggregationapplication 114. This may comprise applying edge detection, artifactdetection, pixel pattern detection, or other functions to the image data131. This may also comprise analyzing metadata 134 associated with theimage data 131 to determine if the image data 131 was saved or stored byan application known to have image editing or manipulation capabilities.The integrity of the image data 131 may also be verified by anotherapproach.

Finally, in box 311, the monitoring data 127 is stored with respect tothe indicated account 128. This may comprise updating or modifying adatabase entry associated with the account 128, or taking another actionwith respect to the account 128.

With reference to FIG. 4, shown is a schematic block diagram of thecomputing environment 101 according to an embodiment of the presentdisclosure. The computing environment 101 includes one or more computingdevices 401. Each computing device 401 includes at least one processorcircuit, for example, having a processor 402 and a memory 404, both ofwhich are coupled to a local interface 407. To this end, each computingdevice 401 may comprise, for example, at least one server computer orlike device. The local interface 407 may comprise, for example, a databus with an accompanying address/control bus or other bus structure ascan be appreciated.

Stored in the memory 404 are both data and several components that areexecutable by the processor 402. In particular, stored in the memory 404and executable by the processor 402 are a data aggregation application,and potentially other applications. Also stored in the memory 404 may bea data store 111 storing accounts 128, and an image knowledge base 147,and other data. In addition, an operating system may be stored in thememory 404 and executable by the processor 402.

It is understood that there may be other applications that are stored inthe memory 404 and are executable by the processor 402 as can beappreciated. Where any component discussed herein is implemented in theform of software, any one or more of a number of programming languagesmay be employed such as, for example, C, C++, C#, Objective C, Java®,JavaScript®, Perl, PHP, Visual Basic®, Python®, Ruby, Flash®, or otherprogramming languages.

A number of software components are stored in the memory 404 and areexecutable by the processor 402. In this respect, the term “executable”means a program file that is in a form that can ultimately be run by theprocessor 402. Examples of executable programs may be, for example, acompiled program that can be translated into machine code in a formatthat can be loaded into a random access portion of the memory 404 andrun by the processor 402, source code that may be expressed in properformat such as object code that is capable of being loaded into a randomaccess portion of the memory 404 and executed by the processor 402, orsource code that may be interpreted by another executable program togenerate instructions in a random access portion of the memory 404 to beexecuted by the processor 402, etc. An executable program may be storedin any portion or component of the memory 404 including, for example,random access memory (RAM), read-only memory (ROM), hard drive,solid-state drive, USB flash drive, memory card, optical disc such ascompact disc (CD) or digital versatile disc (DVD), floppy disk, magnetictape, or other memory components.

The memory 404 is defined herein as including both volatile andnonvolatile memory and data storage components. Volatile components arethose that do not retain data values upon loss of power. Nonvolatilecomponents are those that retain data upon a loss of power. Thus, thememory 404 may comprise, for example, random access memory (RAM),read-only memory (ROM), hard disk drives, solid-state drives, USB flashdrives, memory cards accessed via a memory card reader, floppy disksaccessed via an associated floppy disk drive, optical discs accessed viaan optical disc drive, magnetic tapes accessed via an appropriate tapedrive, and/or other memory components, or a combination of any two ormore of these memory components. In addition, the RAM may comprise, forexample, static random access memory (SRAM), dynamic random accessmemory (DRAM), or magnetic random access memory (MRAM) and other suchdevices. The ROM may comprise, for example, a programmable read-onlymemory (PROM), an erasable programmable read-only memory (EPROM), anelectrically erasable programmable read-only memory (EEPROM), or otherlike memory device.

Also, the processor 402 may represent multiple processors 402 and/ormultiple processor cores and the memory 404 may represent multiplememories 404 that operate in parallel processing circuits, respectively.In such a case, the local interface 407 may be an appropriate networkthat facilitates communication between any two of the multipleprocessors 402, between any processor 402 and any of the memories 404,or between any two of the memories 404, etc. The local interface 407 maycomprise additional systems designed to coordinate this communication,including, for example, performing load balancing. The processor 402 maybe of electrical or of some other available construction.

Although the data aggregation application 114, and other various systemsdescribed herein may be embodied in software or code executed by generalpurpose hardware as discussed above, as an alternative the same may alsobe embodied in dedicated hardware or a combination of software/generalpurpose hardware and dedicated hardware. If embodied in dedicatedhardware, each can be implemented as a circuit or state machine thatemploys any one of or a combination of a number of technologies. Thesetechnologies may include, but are not limited to, discrete logiccircuits having logic gates for implementing various logic functionsupon an application of one or more data signals, application specificintegrated circuits (ASICs) having appropriate logic gates,field-programmable gate arrays (FPGAs), or other components, etc. Suchtechnologies are generally well known by those skilled in the art and,consequently, are not described in detail herein.

The flowcharts of FIGS. 2 and 3 show the functionality and operation ofan implementation of portions of the data aggregation application 114.If embodied in software, each block may represent a module, segment, orportion of code that comprises program instructions to implement thespecified logical function(s). The program instructions may be embodiedin the form of source code that comprises human-readable statementswritten in a programming language or machine code that comprisesnumerical instructions recognizable by a suitable execution system suchas a processor 402 in a computer system or other system. The machinecode may be converted from the source code, etc. If embodied inhardware, each block may represent a circuit or a number ofinterconnected circuits to implement the specified logical function(s).

Although the flowcharts of FIGS. 2 and 3 show a specific order ofexecution, it is understood that the order of execution may differ fromthat which is depicted. For example, the order of execution of two ormore blocks may be scrambled relative to the order shown. Also, two ormore blocks shown in succession in FIGS. 2 and 3 may be executedconcurrently or with partial concurrence. Further, in some embodiments,one or more of the blocks shown in FIGS. 2 and 3 may be skipped oromitted. In addition, any number of counters, state variables, warningsemaphores, or messages might be added to the logical flow describedherein, for purposes of enhanced utility, accounting, performancemeasurement, or providing troubleshooting aids, etc. It is understoodthat all such variations are within the scope of the present disclosure.

Also, any logic or application described herein, including the dataaggregation application 114, that comprises software or code can beembodied in any non-transitory computer-readable medium for use by or inconnection with an instruction execution system such as, for example, aprocessor 402 in a computer system or other system. In this sense, thelogic may comprise, for example, statements including instructions anddeclarations that can be fetched from the computer-readable medium andexecuted by the instruction execution system. In the context of thepresent disclosure, a “computer-readable medium” can be any medium thatcan contain, store, or maintain the logic or application describedherein for use by or in connection with the instruction executionsystem.

The computer-readable medium can comprise any one of many physical mediasuch as, for example, magnetic, optical, or semiconductor media. Morespecific examples of a suitable computer-readable medium would include,but are not limited to, magnetic tapes, magnetic floppy diskettes,magnetic hard drives, memory cards, solid-state drives, USB flashdrives, or optical discs. Also, the computer-readable medium may be arandom access memory (RAM) including, for example, static random accessmemory (SRAM) and dynamic random access memory (DRAM), or magneticrandom access memory (MRAM). In addition, the computer-readable mediummay be a read-only memory (ROM), a programmable read-only memory (PROM),an erasable programmable read-only memory (EPROM), an electricallyerasable programmable read-only memory (EEPROM), or other type of memorydevice.

It should be emphasized that the above-described embodiments of thepresent disclosure are merely possible examples of implementations setforth for a clear understanding of the principles of the disclosure.Many variations and modifications may be made to the above-describedembodiment(s) without departing substantially from the spirit andprinciples of the disclosure. All such modifications and variations areintended to be included herein within the scope of this disclosure andprotected by the following claims.

Therefore, the following is claimed:
 1. A system, comprising: at leastone computing device, configured to at least: obtain, from a mobiledevice, at least one image capturing an image of at least a portion of avehicle; generate from the image of the at least a portion of a vehicleimage data of a feature of the vehicle and image data of an odometerdisplay of the vehicle; generate from the image data of the odometerdisplay an odometer reading; verify that the vehicle corresponds to avehicle identification as a function of the image data of a feature ofthe vehicle by comparing the image data of the feature of the vehicle toan image knowledge base, wherein the at least one image is a first atleast one image of the vehicle and the image knowledge base comprises asecond at least one image depicting at least a portion of the vehicle,and finding a match between the second at least one image depicting atleast a portion of the vehicle and the image data of a feature of thevehicle of the first at least one image of the at least a portion of thevehicle; and associate the odometer reading with the vehicleidentification.
 2. The system of claim 1, wherein the second at leastone image depicting at least a portion of the vehicle corresponds to ashared make, model or year of the vehicle.
 3. The system of claim 1,wherein the at least one computing device is further configured to atleast associate the odometer reading with an account corresponding tothe vehicle.
 4. The system of claim 1, wherein the at least onecomputing device is further configured to determine from the odometerreading a difference between a temporally earlier odometer readingobtained from a prior obtained image of the vehicle taken at a firstpoint in time and the odometer reading taken at a subsequent secondpoint in time.
 5. The system of claim 4, wherein the difference is thenumber of miles the vehicle was driven between the first point in timeand the second point in time.
 6. The system of claim 1, wherein the atleast one computing device is further configured to at least: obtain,from the mobile device, operational data associated with a usage of thevehicle; and associate the operational data with an accountcorresponding to the vehicle.
 7. The system of claim 1, wherein the atleast one computing device is further configured to determine whetherthe first at least one image comprises altered or transformed imagedata.
 8. The system of claim 1, wherein comparing the image data of afeature of the vehicle to the image knowledge base is performed based atleast in part on a machine learning algorithm or an image matchingalgorithm.
 9. A method, comprising: obtaining, by at least one computingdevice, from a mobile device, at least one image capturing an image ofat least a portion of a vehicle; generating from the image of at least aportion of a vehicle image data of a feature of the vehicle and imagedata of an odometer display; generating from the image data of theodometer display an odometer reading; verifying, by the at least onecomputing device, that the vehicle corresponds to a vehicleidentification as a function of the image data of a feature of thevehicle by comparing the image data of the feature of the vehicle to animage knowledge base, wherein the at least one image is a first at leastone image of the vehicle and the image knowledge base comprises a secondat least one image depicting the vehicle, and finding a match betweenthe second at least one image depicting at least a portion of thevehicle and the image data of a feature of the vehicle of the first atleast one image of the vehicle; and associating the odometer readingwith the vehicle identification.
 10. The method of claim 9, wherein thesecond at least one image depicting at least a portion of the vehiclecorresponds to a shared make, model or year of the vehicle.
 11. Themethod of claim 10, further comprising associating, by the at least onecomputing device, the odometer reading with an account corresponding tothe vehicle.
 12. The method of claim 9, further comprising determiningby the at least one computing device, a difference between the odometerreading and an odometer reading obtained from a temporally earlierodometer reading obtained from a prior image of the vehicle taken at afirst point in time and the odometer reading taken at a subsequentsecond point in time.
 13. The method of claim 12, wherein the differenceis the number of miles the vehicle was driven between the first point intime and the second point in time.
 14. The method of claim 10, furthercomprising: obtaining, by the at least one computing device, from themobile device, operational data associated with a usage of the vehicle;and associating, by the at least one computing device, the operationaldata with an account corresponding to the vehicle.
 15. The method ofclaim 10, further comprising determining, by the at least one computingdevice, whether the first at least one image comprises altered ortransformed image data.
 16. The method of claim 10, wherein comparingthe at least one image of a feature of the vehicle to the imageknowledge base is performed based at least in part on a machine learningalgorithm or an image matching algorithm.